Continuous pulp digesters in the pulp and paper industry are used for cooking wood chips to produce pulp which in turn is used for making paper. The aim of cooking is to remove the lignin component present in the wood chips. Lignin is a complex compound present in the wood and acts as a binding material for the cellulose fibers. Wood chips along with the cooking liquor are fed continuously at the top of the digester and the cooked wood chips are removed as pulp product from the bottom of the digester unit. In the dual vessel configuration, the impregnation process is carried out in a separate vessel followed by another vessel containing (e.g., consisting of) upper, lower and washing sections. In general, the cooking process involves removal of lignin from the wood chips by the cooking liquor. During cooking in the continuous pulp digester, the chips always flow top-down, while the flow of the cooking liquor is either co-current or countercurrent for various sections involved. The heat used for cooking is provided by heating the cooking liquor by taking out and recirculating it back. Part of the liquor after extracting the wood components is taken out for further processing. Because of the complex nature of this process, a continuous pulp digester is characterized by highly nonlinear behavior. This, in addition to factors such as presence of long dead time, strong interactions between the process variables, and unmeasured changes in the characteristics of the wood chips, can make the control of digester very difficult.
The digester system has process parameters, such as model states with a particular concentration of one or more components in solid, liquid and gas phase, temperatures, pressures, flow rates of the white liquor and wash liquor, the recirculation flow rates and temperatures, the flow rate of the steam in the heat exchangers, effective alkali and active alkali of white liquor and wash liquor, etc.; quality parameters such as kappa number, consistency, pulp strength, effective alkali and active alkali of the extraction streams, etc.; performance parameters such as energy efficiency, yield, inventory consumption and operating cost, etc.; and model parameters such as reaction rate coefficients, effectiveness factors, diffusion and heat transfer coefficient. All of these parameters (i.e., process parameters, quality parameters, performance parameters and model parameters) are collectively referred to as digester parameters herein.
A known practice in the paper industry is to specifically control the kappa number of the pulp at the bottom outlet or blowline of the digester by manipulating the temperature of circulating cooking liquor flows at different sections and flow rates of make-up white liquor and wash liquor to different sections of the digester. The set-points for temperatures and flow rates of white liquor/wash liquor are adjusted based on operator experience in an ad-hoc fashion. However, recently, multivariable model predictive control strategies have also been proposed to improve the control of the digester. Patents and publications related to control of processes in a digester are as follows:
U.S. Pat. No. 5,301,102 describes the use of step response models and periodic measurements of kappa number and effective alkali of cooking liquor to control the kappa number of pulp produced from a Kamyr digester.
US Publication No. 20050034824 uses a method based on on-line analyzers, dead time compensators, decouplers and a look up table (similar to fuzzy logic rules) of the effect of various manipulated variables on digester quality and performance parameters to achieve desired performance of the digester.
U.S. Pat. No. 6,447,639 relates to the application of heat and ion mobility spectrometry to calculate an amount of cooking liquor added based on the on-line determination of characteristics of chips being fed into the pulp digester.
U.S. Pat. No. 4,752,357 describes a method for determining a degree of cooking to which pulp has been in the digestion process. This can be very useful to establish appropriate predictive control action.
Other known methods reported in the literature deal with application of techniques such as model predictive control using linear and nonlinear models, inferential control, and optimization of the operating conditions to produce pulp of a desired kappa number from a continuous pulp digester.
Continuous pulp digester simulation models of different complexities have also been reported for different applications such as monitoring and control. Recently, Padhiyar et al., (2006) proposed some strategies which aim at controlling the Kappa number profile at various cooking zones of the digester, instead of just controlling it at the blow line. This could facilitate faster process disturbance rejection as corrective action will be initiated much earlier than the consequences are manifested on the Kappa number in the blow line. Such a distributed control strategy will also promote faster and efficient transient operation during grade change. However, the strategy is limited to the proposal of using the kappa number profile, and does not extend to teach how to assign optimal set-points for various controllers. As discussed earlier, assigning set-points has been based on operator experience.
To summarize, known reported approaches are mainly focused on controlling the Kappa number only in the blow line section and on further improvements by controlling the profile of various selected properties like the Kappa number and yield along a length of the digester as well.
The control of the profile should be done optimally to ensure various objectives of the plant such as superior quality and performance, and processes are controlled on-line to meet specifications as desired. However, the control of the profile should be addressed at one or more levels during the continuous digestion process corresponding to one or more sections involved therein during the process. The optimization and control with more than one quality parameter or performance parameter or process parameter involves some ability to deal with the complexities of the plant process, optimization problem formulation and trade offs involved in dealing with conflicting specifications. Exemplary embodiments as disclosed herein are directed to addressing this aspect.